Enhancing Workflow Efficiency through Machine Learning- Based Email Sorting

Abstract

The present scenario of email management poses significant challenges due to the overwhelming volume of emails received daily across various domains. Traditional manual email sorting and classification methods are time-consuming, inefficient, and prone to errors. One of the critical limitations of the present scenario is the reliance on rule-based or keyword-based approaches for email classification. These methods often need more flexibility and help to adapt to the dynamic nature of email content, leading to inaccurate classification results. Additionally, the sheer volume of emails and the presence of spam, phishing attempts, and irrelevant messages exacerbate the difficulty of effectively categorizing emails. The problem addressed in this project is needing a more efficient and accurate email classification system to automate organizing and prioritizing incoming emails. The goal is to develop a solution that overcomes the limitations of existing approaches and enhances productivity by streamlining email management workflows. The proposed solution involves applying machine learning techniques, specifically natural language processing (NLP) and supervised learning algorithms, to classify emails into predefined categories automatically. By training a machine learning model on a labelled dataset of emails, the system can learn to recognize patterns and relationships within the email content, allowing for more accurate classification. Furthermore, the proposed solution aims to leverage advanced features such as sentiment analysis, entity recognition, and context-aware classification to improve the granularity and effectiveness of email categorization.